A New Multivariate Nonlinear Time Series Model for Portfolio Risk Measurement: The Threshold Copula‐Based TAR Approach

Wong, Shiu Fung; Tong, Howell; Siu, Tak Kuen; Lu, Zudi

doi:10.1111/jtsa.12206

Cited by 9 publications

(2 citation statements)

References 31 publications

(41 reference statements)

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“…Thus, Tong [5] first proposed the threshold copula method to introduce the threshold principle into nonlinear time series analysis, and more research can be found in Tong and Lim [6]. After that, Wang [7] and others proposed a nonlinear time series model based on threshold copula to evaluate and predict quantitative risk measures in financial portfolios based on the study above and with the flexibility to incorporate nonlinearity into a univariate time series correlation structure.…”

Section: Introductionmentioning

confidence: 99%

A study on systematic risk among financial sub-markets based on the SETAR-Copula model

Xue

Xia

et al. 2023

International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022)

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Along with the gradual development of the international oil market in the direction of financialization, more and more investors are investing and trading oil as a financial asset, resulting in crude oil price fluctuations affecting the transfer of input funds between stock markets. In turn, the transfer of funds in stock markets will trigger changes in a country's exchange rate. Therefore, crude oil is an essential strategic resource, and its price fluctuations are bound to move the country's stock and currency markets, thus bringing systemic risk. Therefore, based on the industry perspective, the SETAR-Copula model is used to portray the nonlinear characteristics between the exchange rate, WTI crude oil futures price index and SSE index, and the sample data are divided into different intervals according to the threshold parameters in the model. The Vine Copula function is combined to measure the conditional value-at-risk CoVaR and risk spillover CoVaR under various combinations of intervals. The study shows that the stability of the exchange market and the stock market in China can weaken the impact of the volatility of the international crude oil market when it is in a fastrising stage.

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Section: Introductionmentioning

confidence: 99%

A study on systematic risk among financial sub-markets based on the SETAR-Copula model

Xue

Xia

et al. 2023

International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022)

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“…Among different nonlinear autoregressive models, self‐exciting threshold autoregressive (SETAR) models have gathered significant attention because of the simplicity of their definition and, at the same time, enough flexibility of their representation to explain relatively complex time series processes. Following the growing needs of the SETAR models, Arnold and Günther () extended the idea to the multivariate scenario to define multivariate SETAR (MSETAR) models and discussed their importance in applications; see also Chan et al (), Baragona and Cucina (), Addo (), and Wong et al ().…”

Section: Introductionmentioning

confidence: 99%

On the Stationary Marginal Distributions of Subclasses of Multivariate Setar Processes of Order One

Das

Genton

2019

Journal Time Series Analysis

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To introduce more flexibility in process‐parameters through a regime‐switching behavior, the classical autoregressive (AR) processes have been extended to self‐exciting threshold autoregressive (SETAR) processes. However, the stationary marginal distributions of SETAR processes are usually difficult to obtain in explicit forms and, therefore, they lack appropriate characterizations. The stationary marginal distribution of a multivariate (d‐dimensional) SETAR process of order one ()MSETARdfalse(1false) with multivariate normal innovations is shown to belong to the unified skew‐normal (𝒮𝒰𝒩) family and characterized under a fairly broad condition. This article also characterizes the stationary marginal distributions of a subclass of the MSETARdfalse(1false) processes with symmetric multivariate stable innovations. To characterize the stationary marginal distributions of these processes, the authors show that they belong to specific skew‐distribution families, and for a given skew‐distribution from the corresponding family, an MSETARdfalse(1false) process, with stationary marginal distribution identical to the given skew‐distribution, can be associated. Furthermore, this article illustrates a diagnostic of an MSETAR2false(1false) model using the corresponding stationary marginal density.

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Vector time series models

2019

Multivariate Time Series Analysis and Applications

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A New Multivariate Nonlinear Time Series Model for Portfolio Risk Measurement: The Threshold Copula‐Based TAR Approach

Cited by 9 publications

References 31 publications

A study on systematic risk among financial sub-markets based on the SETAR-Copula model

A study on systematic risk among financial sub-markets based on the SETAR-Copula model

On the Stationary Marginal Distributions of Subclasses of Multivariate Setar Processes of Order One

Vector time series models

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